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Volume 50 Issue 8
Aug.  2024
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Journal of Beijing University of Aeronautics and Astronautics  > 2024  >  50(8): 2391-2403.
HOU Z Q，MA J Y，HAN R X，et al. A fast long-term visual tracking algorithm based on deep learning[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（8）：2391-2403 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0645
Citation: HOU Z Q，MA J Y，HAN R X，et al. A fast long-term visual tracking algorithm based on deep learning[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（8）：2391-2403 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0645
shu

A fast long-term visual tracking algorithm based on deep learning

doi: 10.13700/j.bh.1001-5965.2022.0645
  • 1.

    School of Computer Science and Technology，Xi’an University of Posts and Telecommunications，Xi’an 710121，China

  • 2.

    Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing，Xi’an University of Posts and Telecommunications，Xi’an 710121，China

  • 3.

    College of Information and Navigation，Air Force Engineering University，Xi’an 710077，China

Funds:  National Natural Science Foundation of China (62072370)
More Information
  • Corresponding author: E-mail：hzq@xupt.edu.cn
  • Received Date: 27 Jul 2022
  • Accepted Date: 26 Nov 2022
  • Publish Date: 10 Jan 2023
    Abstract

  • Current deep learning-based visual tracking algorithms have difficulty tracking the target accurately in real-time in complex long-term monitoring environments including target size change, occlusion, and out-of-view. To solve this problem, a fast long-term visual tracking algorithm is proposed, which consists of a fast short-term tracking algorithm and a fast global re-detection module. First, as a short-term tracking algorithm, the attention module of second-order channel and region spatial fusion is added to the base algorithm SiamRPN. Then, in order to make the improved short-term tracking algorithm have a fast long-term tracking ability, the global re-detection module based on template matching proposed in this paper is added to the algorithm, which uses a lightweight network and fast similarity judgment method to speed up the re-detection rate. The proposed algorithm is tested on five datasets (OTB100, LaSOT, UAV20L, VOT2018-LT, and VOT2020-LT). With an average tracking speed of 104 frames per second, the experimental findings demonstrate the algorithm's outstanding long-term tracking performance.

     

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